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Openligadb

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already provide safety profile (readOnly, idempotent, non-destructive). Description adds useful behavioral details like returning full schemas with examples and being ready to call directly, but no major behavioral revelations beyond that.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with main purpose and usage guidance. The list of example domains is a bit lengthy but still efficient. Minor verbosity prevents a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully explains what is returned (names, descriptions, full schemas with examples) and the tool's purpose. For a discovery tool with many aliases but only one required param, it is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents all parameters including aliases. Description does not add significant meaning beyond the schema (e.g., when to use each alias). Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it finds tools by describing the data or task, listing many domains. It distinguishes itself from sibling tools (which are specific research tools) by being a meta-tool for discovering other tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set (not just one answer),' providing clear when-to-use guidance and implying alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.9/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions differentiating similar tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between ask_pipeworx and ask_pipeworx_beta, as both serve as universal routers with only minor routing improvements.

Naming Consistency2/5

Tool names follow inconsistent patterns: some use snake_case (ask_pipeworx, entity_profile), others use lowercase single words (forget, recall), and some use camelCase (bet_research, deep_research). This mix of conventions makes the naming scheme unpredictable.

Tool Count3/5

With 35 tools, the server covers a wide range of data sources, but the count feels slightly heavy for the apparent scope. Several tools serve meta-purposes (discover_tools, suggest_questions) or specialized functions (polymarket_arbitrage), adding to the complexity.

Completeness4/5

The tool set offers comprehensive coverage for financial, economic, drug, and news data, including comparison and grounding capabilities. However, the football-related tools are limited to German leagues, leaving a minor gap for other sports or regions.

Resources